Operation optimization method of comprehensive energy system in agricultural park based on deep learning
LIU Zhaoyu
WANG Lei
WANG Kun
Abstract：[Objective]Agricultural parks,characterized by abundant renewable energy resources,play a pivotal role in advancing green and low-carbon transformation under the carbon peaking and carbon neutrality goals.However,current agricultural parks face challenges such as low energy utilization efficiency,imbalanced multi-energy distribution,and insufficient local renewable energy accommodation capacity,which hinder agricultural productivity and sustainable development.To address these issues,this study proposed a deep learning-based optimization method for constructing a more economical and low-carbon integrated energy system(IES)in agricultural parks.[Methods]First,a multi-objective optimal scheduling model for agricultural park IES was established,integrating economic objectives such as fuel costs of gas turbines,grid interaction costs,and equipment maintenance costs,while formulating mathematical constraints for multi-energy coupling systems.Second,an improved long short-term memory(LSTM)neural network was employed to predict photovoltaic/wind power outputs and load demands.The hyperparameters of the LSTM model,including hidden layer units and learning rates,were dynamically optimized using quantum particle swarm optimization(QPSO)to enhance prediction accuracy.Finally,to mitigate premature convergence in the traditional golden sine algorithm(GSA),an enhanced GSA algorithm was proposed by incorporating Lévy flight strategies to expand the search space and designing dynamic weight mechanisms to balance global exploration and local exploitation capabilities.[Results]Case studies demonstrate that the errors of improved QPSO-LSTM prediction model are controlled within 5％,outperforming traditional optimization algorithms in accuracy and robustness against local optima.For scheduling optimization,the enhanced GSA algorithm achieves a 69.7％reduction in daily operational costs and a 27.9％improvement in local renewable energy accommodation rates compared to unscheduled scenarios,significantly surpassing conventional GSA and other methods.These results validate the algorithm's effectiveness in balancing economic efficiency and low-carbon requirements for multi-energy coordination.[Conclusion]The proposed deep learning-based optimization framework enables high-precision power prediction and cost-effective scheduling for agricultural park IES.It significantly reduces operational costs while enhancing renewable energy utilization,demonstrating superior performance in synergizing economic and low-carbon objectives.This study provides a reliable technical pathway for the efficient and sustainable operation of agricultural park IES.
Keywords：agricultural industrial parkintegrated energy systemoperation optimizationmathematical modelquantum particle swarm optimization algorithmlong short-term memory neural networkLévy flightgolden sine algorithm
Publication Date：2025-07-25
Online Publishing Date：2025-09-18(First online date of this platform, not the publication date of the document)
Pages：8( 470-477 )